:We have implemented our method in Pytorchhttp://pytorch.org/. Since the re-sampled CT image were often large, we used a 176x176x176 crop during training. The cropping region was centered on the kidney tumor label (with probability 0.8), on any foreground (with probability 0.1) and on background (with probability 0.1). We found it important to sample more frequently from the tumor region. The model was trained on 8 NVIDIA Tesla V100 16GB GPUs (DGX-1 server). We used a batch size of 8 and the Adam optimization algorithm with the initial learning rate of \( \alpha _{0} = 5e-5\)  that was further decreased according to \(\alpha = \alpha _{0} *\left(1-e/N_{e}\right)^{0.9}\)  [1]}, where \(e\)  and \(N_{e}\)  denote the current epoch counter total number of epochs (300 in our case). During inference, we have leveraged
test time augmentation (TTA) and have used an ensemble of 5 models to further improve the results.
